The New Problems AI Is Creating (And How People Are Solving Them)

**Nathaniel Whittemore** (0:00)
Last year at this time, AI was a very different place. ChatGPT 5 had just launched, to not much acclaim at all, people were really upset that GPT 4 is being deprecated, and there was so much growing conversation and consternation, frankly, about the potential of an AI bubble. And on top of all that, there were some companies out there that were still trying to convince themselves that AI was overhyped and just not going to be a thing. Now a year on from that, the conversation is very different. Not only have the models advanced, not only have the use cases shifted to the agentic, living up to the promise that's been lurking for years, but the businesses that are harnessing AI have gotten so much more sophisticated in the questions they're asking. In fact, over the last year, we've gone from in many cases not even asking the right questions, to actively solving the new problems that emerge for new work patterns that come alongside specifically agentic AI.
Easy production causing an AI slot problem, institute a new AI writing policy, over usage of top models costing too much, come up with new ways to allocate tokens and intelligence to different parts of the organization. Today, we're going to dig into not only the current challenges of AI, but how companies are actually solving them.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, let's see, Section, Robots and Pencils and HyperAgent. To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple podcasts. To learn more about sponsoring the show, send us a note at sponsors at aidailybrief.ai.
One of the things that I like about this moment that we're in with AI, is that we're through the first wave of a lot of, call it less than useful conversations. Even at this time last year, there was still a ton of debate about whether this AI thing was going to be a thing. Now, of course, that wasn't really a debate around these parts, but you still had enterprises all over the place, kind of holding out hope that this would just be yet another trend that they would be rewarded for not having dug in around. Now, of course, that is not how it has played out. In the past year and especially the past eight months, we have rocketed right on through the first stage of AI into the agentic era with all sorts of attendant consequences. Now, a lot of what's happening now is incredibly powerful. A lot of the work inside businesses of all shapes and sizes is figuring out how to take advantage of capabilities that simply were not there before. And yet, no new technology, AI included, is solely in the business of solving problems no matter how powerful it is.
Instead, new technologies solve lots of old problems while, in many cases, through the opportunities they create, also creating new challenges. And when we discuss topics like bot-sitting or AI slop, we are firmly, then, in the discussion of how to deal with the problems that come along with AI's opportunities.
Today, we are going to look at a bunch of changes in how companies and businesses specifically are thinking about AI and what they are doing to solve some of those problems. And by way of kicking off the conversation, we are going to start with a piece from EY called Four AI Misconceptions That Deserve Greater Scrutiny, with a specific focus on the first three. I think these are great examples of things, the conventional wisdom around which is quickly shifting and shifting for the better. The first misconception that EY points out is that AI will immediately generate a productivity boom. They write, The assumption that AI will immediately generate a surge in productivity is difficult to reconcile with economic history. Major technological revolutions rarely produce economy-wide gains overnight. It took nearly a century for the steam engine to translate into sustained productivity growth in Britain, roughly five decades for electricity to reshape industrial production, and close to a decade before the computer revolution produced measurable improvements in aggregate productivity.
The first stage, they explain, of any technology revolution is the buildout of the infrastructure that enables it. In the case of AI, that means expanding data centers, semiconductor manufacturing, electricity generation, cloud computing capacity, and digital infrastructure. It also requires developing the talent needed to deploy and manage these technologies before they can diffuse across the broader economy.
So in this section, EY is talking about productivity in two very different ways. They're talking about measurable productivity showing up in overall macroeconomic numbers, but I think that the more relevant part for our discussion at least is an immediate boom in productivity inside the organization. I believe that what most organizations are finding is that just like the capabilities of AI are jagged, so too is the productivity enhancement of AI. There are areas which, for any organization that has invested any amount of time in AI, the gains are just immediate and transparent and huge. There are other areas where even if one believes AI will impact that area eventually, remains stubbornly stuck in the way that they'd always done things. Moreover, organizations are going through the messy and complicated and time-consuming process of figuring out how to integrate new ways of agentic working with new types of human oversight and management. We're not seeing the one-to-one switch from humans doing jobs to agents doing jobs that some people imagine we would, and so figuring out how to take advantage of all the new opportunity creates a whole new set of work that in the short term at least in many cases fills in any time gains that you otherwise would get one from productivity in previous tasks. Which is not to say that this is all a wash and that productivity is going to be neutral. We are very clearly in a transitional phase, and there's just going to be an immense amount of work on the path to the new norms and how we do things. By and large, the organizations that I'm interacting with have fully embraced that fact and are now trying to work one by one through those challenges so that they can really take advantage of AI rather than sitting around lamenting why they're not getting as much as they hoped from it.